Integrated Multimodal Travel Behavior Analysis under Mobility as a Service

移动即服务下的综合多式联运行为分析

• 批准号: RGPIN-2022-04553
• 负责人: Wang, Bobin
• 金额: $ 1.97万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754531
• 关键词: Integrated; Multimodal; Travel; Behavior; Analysis

Mobility as a Service (MaaS) is an important means of urban transportation sustainable development that has become popular in the global transportation field recently. By integrating services of multiple providers (e.g., taxi, rental vehicle, bicycle, public transit, car- or bike-sharing, etc.) and enabling searching, booking, and payment through a single digital platform, MaaS provides door-to-door seamless mobility solutions that allows travelers to arrive destinations as quickly as possible. However, the MaaS conceptual framework and the potential influence of MaaS on people's travel behavior are not well elaborated in Canada, and current models and analysis approaches require improvement to explain the travel decision-making rules under big data. Therefore, the long-term objective of this program is to develop innovative models, new data methods, and applicable tools to generate an efficient, intelligent, and sustainable multimodal transportation system in Canada. The content of this program includes two work packages. Package 1 develops the forefront knowledge to analyze the travel behavior decisions of travelers, considering the diversity of human factors (gender, age, region, etc.) to understand their travel preferences. Package 2 focuses on new mathematical models of travel behavior analysis to better predict decisions. The research results of package 2 can provide support for package 1 to better understand the multimodal travel decisions under MaaS. More specifically, the short-term objectives for this program are to: 1) Develop forefront behavioral models to understand consumer's decision-making under MaaS. 2) Develop multi-source data fusion techniques to integrate large-scale detector data with small-scale survey data. 3) Propose new feature selection methods to select the most important predictors for travel decisions under MaaS. 4) Develop behavioral theory-driven machine learning approaches that bridge machine learning and behavior models. This program can contribute to achieving the economic, social, and environmental goals of sustainable mobility in Canada. The research results can provide theoretical support for the government to make long-term and short-term policies on MaaS. The methodological pipeline developed is valuable to engineering domains and intelligent transport system applications, which provides an important fundament to other researchers for further development. In addition, this program will promote fundamental interdisciplinary advances to train HQP through the integration of artificial intelligence knowledge with domain expertise in transportation engineering.

移动即服务（Mobility as a Service, MaaS）是近年来在全球交通领域流行起来的城市交通可持续发展的重要手段。通过整合多个供应商的服务（如出租车、租车、自行车、公共交通、汽车或自行车共享等），并通过一个单一的数字平台实现搜索、预订和支付，MaaS提供门到门的无缝移动解决方案，使旅行者能够尽快到达目的地。然而，加拿大并没有很好地阐述MaaS的概念框架和MaaS对人们出行行为的潜在影响，现有的模型和分析方法需要改进才能解释大数据下的出行决策规则。因此，该项目的长期目标是开发创新模型、新的数据方法和适用工具，以在加拿大建立一个高效、智能和可持续的多式联运系统。该计划的内容包括两个工作包。Package 1开发最前沿的知识来分析旅行者的旅行行为决策，考虑到人为因素（性别，年龄，地区等）的多样性，了解他们的旅行偏好。包2侧重于新的数学模型的旅行行为分析，以更好地预测决策。包2的研究结果可以为包1提供支持，以更好地理解MaaS下的多式联运出行决策。更具体地说，这个项目的短期目标是：1)建立最前沿的行为模型，以了解消费者在MaaS下的决策。2)发展多源数据融合技术，实现大尺度探测器数据与小尺度勘测数据的融合。3)提出新的特征选择方法，选择MaaS下最重要的出行决策预测因子。4)开发行为理论驱动的机器学习方法，将机器学习和行为模型连接起来。该项目有助于实现加拿大可持续交通的经济、社会和环境目标。研究结果可为政府制定MaaS的长期和短期政策提供理论支持。所开发的管道方法对工程领域和智能交通系统的应用具有重要的价值，为其他研究人员的进一步发展提供了重要的基础。此外，该项目将通过整合人工智能知识与交通工程领域的专业知识，促进跨学科的基础进步，以培养HQP。